An Improved Smoothed $\ell^0$ Approximation Algorithm for Sparse Representation

Sparsity recovery by iterative orthogonal projections of nonlinear mappings

“…To show the effectiveness of our algorithm we directly compared it with some algorithms for sparsity recovery well known in literature, as Matching Pursuit (MP) [10], Orthog onal Matching Pursuit (OMP) [11], Stagewise Orthogonal Matching Pursuit (StOMP) [12], LASSO [13], LARS [13], Smoothed LO (SLO) [7] and Improved SLO (ISL02) [14].…”

One-bit compressive sampling via ℓ 0 minimization

“…We remark that Algorithm 3, RFPI, LP, and GAMP do not require the knowledge of sparsity of original signals. (19). Figure 2a demonstrates that the GAMP performs best, the BIHT and Algorithm 3 perform similarly and exhibit much better performance than the LP and RFPI in terms of SNR values.…”

“…Let B be the (m + 1) × n matrix defined by (10), let F be the Log-Det function defined by (19), and let {x (k) : k ∈ N} be the sequence generated by Algorithm 1.…”

“…The accuracy of all test algorithms is measured by the average of values of these four metrics over 100 trials unless otherwise noted. For all figures in this section, results by the BIHT, RFPI, LP, GAMP, and Algorithm 3 with the Mangasarian function (17) and the Log-Det function (19) are marked by the symbols " , " " , " " , " " , " "•, " and " , " respectively.…”

Two-dimensional SLIM with application to pulse Doppler MIMO radars

“…On the other hand, the smoothed L0 (SL0) algorithm has been presented for two-dimensional (2D) sparse problems [23]. In this algorithm, a discontinuous l 0 -norm function is approximated by a continuous one and then a sparse solution is reached using the steepest ascent algorithm followed by a projection onto a feasible set [24][25][26][27]. In [28], this algorithm has been applied to pulse Doppler radars with a lot of advantages such as target velocity extraction and pulse integration.…”